A web-based tool to design and analyze single- and double-stage acceptance sampling plans

Acceptance sampling plans are used to determine whether production lots can be accepted or rejected. Existing tools only provide a limited functionality for the two-point design and the risk analysis of such plans. In this article, a web-based tool is presented to study single- and double-stage sampling plans. In contrast to existing solutions, the tool is an interactive applet that is freely available. Analytic properties are derived to support the development of search strategies for the design of double-stage sampling plans that are more efficient and accurate in comparison with existing routines. Several case studies are presented

One-class classification of point patterns of extremes

Novelty detection or one-class classification starts from a model describing some type of 'normal behaviour' and aims to classify deviations from this model as being either novelties or anomalies. In this paper the problem of novelty detection for point patterns S = {X-1 ,..., X-k} subset of R-d is treated where examples of anomalies are very sparse, or even absent. The latter complicates the tuning of hyperparameters in models commonly used for novelty detection, such as one-class support vector machines and hidden Markov models. To this end, the use of extreme value statistics is introduced to estimate explicitly a model for the abnormal class by means of extrapolation from a statistical model X for the normal class. We show how multiple types of information obtained from any available extreme instances of S can be combined to reduce the high false-alarm rate that is typically encountered when classes are strongly imbalanced, as often occurs in the one-class setting (whereby 'abnormal' data are often scarce). The approach is illustrated using simulated data and then a real-life application is used as an exemplar, whereby accelerometry data from epileptic seizures are analysed - these are known to be extreme and rare with respect to normal accelerometer data

Relative Age Effect on European Adolescents’ Social Network

We contribute to the literature on relative age effects on pupils’ (non-cognitive) skills formation by studying students’ social network. We investigate data on European adolescents from the Health Behaviour in School Aged Children survey and use an instrumental variables approach to account for endogeneity of relative age while controlling for confounders, namely absolute age, season-of-birth, and family socio-economic status. We find robust evidence that suggests the existence of a substitution effect: the youngest students within a class e-communicate more frequently than relatively older classmates but have fewer friends and meet with them less frequently

Younger and Dissatisfied? Relative Age and Life-satisfaction in Adolescence

This is the first study to investigate whether age gaps between classmates (that is, relative age) affect life-satisfaction gaps in adolescence. To this end, we analyse data from the multi-country Health Behaviour in School-Aged Children (HBSC) survey. We find evidence that relative age negatively impacts adolescents’ life-satisfaction. A twelve-month age gap decreases life-satisfaction, rated on a 0-10 scale, by 0.3 points. This negative effect is consistent across countries. Finally, this negative effect does not decrease with the increase in absolute age

Younger and Dissatisfied? Relative Age and Life-satisfaction in Adolescence

This is the first study to investigate whether age gaps between classmates (that is, relative age) affect life-satisfaction gaps in adolescence. To this end, we analyse data from the multi-country Health Behaviour in School-Aged Children (HBSC) survey. We find evidence that relative age negatively impacts adolescents’ life-satisfaction. A twelve-month age gap decreases life-satisfaction, rated on a 0-10 scale, by 0.3 points. This negative effect is consistent across countries. Finally, this negative effect does not decrease with the increase in absolute age

On the Calibration of Probabilistic Classifier Sets

Multi-class classification methods that produce sets of probabilistic classifiers, such as ensemble learning methods, are able to model aleatoric and epistemic uncertainty. Aleatoric uncertainty is then typically quantified via the Bayes error, and epistemic uncertainty via the size of the set. In this paper, we extend the notion of calibration, which is commonly used to evaluate the validity of the aleatoric uncertainty representation of a single probabilistic classifier, to assess the validity of an epistemic uncertainty representation obtained by sets of probabilistic classifiers. Broadly speaking, we call a set of probabilistic classifiers calibrated if one can find a calibrated convex combination of these classifiers. To evaluate this notion of calibration, we propose a novel nonparametric calibration test that generalizes an existing test for single probabilistic classifiers to the case of sets of probabilistic classifiers. Making use of this test, we empirically show that ensembles of deep neural networks are often not well calibrated

Point process models for novelty detection on spatial point patterns and their extremes

Novelty detection is a particular example of pattern recognition identifying patterns that departure from some model of "normal behaviour". The classification of point patterns is considered that are defined as sets of N observations of a multivariate random variable X and where the value N follows a discrete stochastic distribution. The use of point process models is introduced that allow us to describe the length N as well as the geometrical configuration in data space of such patterns. It is shown that such infinite dimensional study can be translated into a one-dimensional study that is analytically tractable for a multivariate Gaussian distribution. Moreover, for other multivariate distributions, an analytic approximation is obtained, by the use of extreme value theory, to model point patterns that occur in low-density regions as defined by X. The proposed models are demonstrated on synthetic and real-world data sets

The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

Opportunistically collected species occurrence data are often used for species distribution models (SDMs) when high-quality data collected through standardized recording protocols are unavailable. While opportunistic data are abundant, uncertainty is usually high, e.g. due to observer effects or a lack of metadata. To increase data quality and improve model performance, we filtered species records based on record attributes that provide information on the observation process or post-entry data validation. Data filtering does not only increase the quality of species records, it simultaneously reduces sample size, a trade-off that remains relatively unexplored. By controlling for sample size in a dataset of 255 species, we were able to explore the combined impact of data quality and sample size on model performance. We applied three data quality filters based on observers' activity, the validation status of a record in the database and the detail of a submitted record, and analyzed changes in AUC, Sensitivity and Specificity using Maxent with and without filtering. The impact of stringent filtering on model performance depended on (1) the quality of the filtered data: records validated as correct and more detailed records lead to higher model performance, (2) the proportional reduction in sample size caused by filtering and the remaining absolute sample size: filters causing small reductions that lead to sample sizes of more than 100 presences generally benefitted model performance and (3) the taxonomic group: plant and dragonfly models benefitted more from data quality filtering compared to bird and butterfly models. Our results also indicate that recommendations for quality filtering depend on the goal of the study, e.g. increasing Sensitivity and/or Specificity. Further research must identify what drives species' sensitivity to data quality. Nonetheless, our study confirms that large quantities of volunteer generated and opportunistically collected data can make a valuable contribution to ecological research and species conservation